The recent rise in data sharing and improved data collection strategies have brought
neuroimaging to the Big Data era [1]}, [2]}. Existing
neuroimaging workflow engines, such as Nipype [3]}, are well suited for
processing the standard compute-intensive neuroimaging pipelines, but lack
incorporated Big Data strategies (i.e. in-memory computing, data locality, and
lazy-evaluation) to improve performance of the increasingly prevalent data-intensive
pipelines. As was noted in [4]}, in-memory computing, coupled
with data locality, can bring significant performance improvements to data intensive
neuroimaging pipelines. In this work, we investigate how performance benchmarks
generalize across Big Data engines. In particular, we study the differences between
Dask [5]} and Apache Spark [6]}, for their suitability in the
processing of neuroimaging pipelines. Our goal is to test whether Spark or Dask has a
clear performance advantage to process Big neuroimaging Data.
